Course #
30545
Section Number
2
Day(s)
M
-
W
Time(s)
1:30pm-2:50pm
Term
Winter 2025
Specialization
Data Analytics
Course Instructor
Syllabus

The objective of this course is to train students to be insightful users of modern machine-learning methods. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. In order to have greater flexibility when analyzing datasets, both frequentist and Bayesian methods are investigated. This class is required for the Data Analytic specialization but is open to all students who have taken the Harris core statistics classes (or the equivalent) and have some exposure to programming.

Notes

Students are required to register for both a lecture (PPHA 30545) and a language-specific discussion (PPHA 30547 in R or PPHA 30548 in Python).

  • PPHA 30547/1L01 - Lab: Machine Learning in R will be offered Fridays from 1:30pm-2:50pm

  • PPHA 30548/1L01 - Lab: Machine Learning in Python will be offered Fridays from 1:30pm-2:50pm

  • PPHA 30548/1L02 - Lab: Machine Learning in Python will be offered Fridays from 9:00am-10:20am

Quarter Title Instructor Day(s) Time(s) Syllabus
Winter 2025 Machine Learning for Public Policy Chris Clapp Monday, Wednesday 10:30am-11:50am Syllabus
Winter 2025 Machine Learning for Public Policy Chris Clapp Monday, Wednesday 1:30pm-2:50pm Syllabus
Winter 2025 Machine Learning for Public Policy Chris Clapp Tuesday, Thursday 9:30am-10:50am Syllabus
Winter 2025 Machine Learning for Public Policy Chris Clapp Tuesday, Thursday 11:00am-12:20pm Syllabus
Winter 2025 Machine Learning for Public Policy Jeff Levy Tuesday, Thursday 2:00pm-3:20pm Syllabus
Winter 2025 Machine Learning for Public Policy Jeff Levy Tuesday, Thursday 3:30pm-4:50pm Syllabus